An inference engine is a core component of an expert system that applies logical rules and reasoning algorithms to the information stored in a knowledge base. Its purpose is to deduce new or additional information, enabling the system to draw conclusions or make decisions based on the provided data.
Inference engines typically operate using two main approaches:
- Forward Chaining – Starting with the known facts and applying rules to infer conclusions or predictions step by step.
- Backward Chaining – Starting with a hypothesis or goal and working backward to determine if the available data and rules support it.
These engines are widely used in AI systems for decision support, diagnostics, and automated reasoning across domains like healthcare, engineering, and business analytics. By mimicking human reasoning processes, inference engines enhance the ability of expert systems to provide actionable insights and solutions.